Automatic Counting Research Articles

Optimizing Instant Herbal Drink Production with IoT-Based Sachet Forming Machines to Increase Productivity and Quality

MSMEs Syarimpon and Melos (Melati Losari) are Micro, Small and Medium Enterprises that focus on the production of herbal drinks in Malang Regency. However, during its development, these MSME players experienced several problems, one of which was the packaging process which was carried out manually. This makes the production process take longer and reduces work efficiency. Apart from that, the process of cutting packaging manually also causes a decrease in the quality of the packaging and its contents. To overcome this problem, one solution is to use a counter machine with an automatic counter. By using this machine, MSMEs can speed up the packaging process, produce precise and neat packaging, and provide the freedom to innovate in determining the size of the packaging to be produced. In the process of using this IoT-based Sachet Forming Machine, it has succeeded in increasing the productivity and quality of the packaging process by taking 3 -5 seconds for one herbal drink sachet so that each day it can increase production to 900 sachets per day. Where the packaging process is still done manually so it takes 4-5 minutes so the packaging process per day is only 50 to 100 PCS of herbal drinks.
 Keywords: Sachet Forming Machine, IoT, MSMEs Syarimpon and Melos

Evaluating the impact of low traffic neighbourhoods in areas with low car ownership: A natural experimental evaluation

BackgroundImproving the environment, including the streetscape, can enable walking and cycling, which impacts health. The aim of this study was to measure the impact of modal filters implemented as part of Low Traffic Neighbourhoods (LTNs) in Southwark, a borough with low car ownership located in Inner London, on driving, walking, and cycling levels. MethodologyThree neighbourhoods (Brunswick Park, North Peckham, East Faraday) introduced modal filters which restricted motorised traffic through-movement but not walking and cycling. Intervention areas were matched to control neighbourhoods with no street changes. Daily traffic, walking, and cycling counts and traffic speeds were collected using automatic counters and video monitors in all neighbourhoods at baseline and one year. In each neighbourhood we delineated ‘project’ and ‘boundary’ streets to capture potential displacement of traffic. We used a difference-in-differences analysis to estimate the effects of the LTNs by calculating the change in travel behaviours in intervention areas relative to control areas. ResultsDaily traffic counts decreased on ‘project’ streets in the intervention areas relative to controls by 860 (95% CI 409, 1312) in Brunswick Park, representing a 56% decrease (IRR 0.44, 95% CI 0.28, 0.68), and by 937 counts (95% CI 328, 1546), representing a 61% decrease (IRR 0.39, 95% CI 0.26, 0.71) in North Peckham. Traffic volumes did not significantly change in East Faraday. Across all three intervention project areas, traffic speeds decreased. Traffic levels remained unchanged on ‘boundary’ streets, with the exception of Brunswick Park, where there was an uplift in traffic levels during weekdays and peak hours. In general, there were no significant changes in daily walking and cycling counts. DiscussionFindings from this study demonstrate that modal filters can reduce traffic speeds and volumes on ‘project’ streets. Although these measures can offer a safer, less polluted, and more inviting environment for pedestrians and cyclists, areas with low car ownership may need further initiatives to boost walking and cycling.

Anchor-free deep convolutional neural network for tracking and counting cotton seedlings and flowers

Accurate counting of plants and their organs in natural environments is essential for breeders and growers. For breeders, counting plants during the seedling stage aids in selecting genotypes with superior emergence rates, while for growers, it informs decisions about potential replanting. Meanwhile, counting specific plant organs, such as flowers, forecasts yields for different genotypes, offering insights into production levels. The overall goal of this study was to investigate a deep convolutional neural network-based tracking method, CenterTrack, for cotton seedling and flower counting from video frames. The network is extended from a customized CenterNet, which is an anchor-free object detector. CenterTrack predicts the detections of the current frame and displacements of detections between the previous frame and the current frame, which are used to associate the same object in consecutive frames. The modified CenterNet detector achieved high accuracy on both seedling and flower datasets with an overall AP50 of 0.962. The video tracking hyperparameters were optimized for each dataset using orthogonal tests. Experimental results showed that seedling and flower counts with optimized hyperparameters highly correlated with those of manual counts (R2 = 0.98 andR2 = 0.95) and the mean relative errors of 75 cotton seedling testing videos and 50 flower testing videos were 5.5 % and 10.8 %, respectively. An average counting speed of 20.4 frames per second was achieved with an input resolution of 1920 × 1080 pixels for both seedling and flower videos. The anchor-free deep convolution neural network-based tracking method provides automatic tracking and counting in video frames, which will significantly benefit plant breeding and crop management.

Artificial intelligence in automatic passenger counting: cost-efficient validation using the partitioned equivalence test

In Automatic Passenger Counting (APC), the demand for very low errors has been fueled by applications like revenue sharing, which amounts to massive annual sums. As a consequence, APC validation costs are increasing and this work presents a solution to increase the efficiency of initial or recurrent, e.g. yearly, validations. The new approach, the partitioned equivalence test, guarantees the same bounded, low user risk while reducing efforts in comparison to established (equivalence) test procedures. This involves a pre-classification step, which selects the more informative bits of data (e.g. footage). Different use cases are evaluated: from entirely manual to algorithmic, artificial intelligence assisted workflows. Already for manual counts, the new approach can be used as a drop-in replacement to reduce validation costs, while savings in algorithmic use cases demonstrated that cost halving is possible. Due to the user risk being controlled no additional technical requirements are introduced.

Correlation of MR pulmonary perfusion in patients with COVID-19 with quantitative assessment of acute phase CT images

INTRODUCTION: In the last decade, there has been an increased interest in new diagnostic techniques for assessing quantitative values in radiology. In particular, accurate quantitative values may be useful to assess anatomical or physiological changes in the lungs in patients with previously treated COVID-19 infection.OBJECTIVE: To test a quantitative semi-automated algorithm for CT imaging in patients with confirmed COVID-19 infection and to compare the results to MR lung perfusion after coronavirus infection.MATERIALS AND METHODS: The data from 100 chest CT scans of patients with COVID-19 were retrospectively analyzed. 3D segmentation of the lungs was carried out with automatic counting of the number of separated pixels in each slice. For quantitative data analysis, classification based on the density value of each pixel according to the Hounsfield scale was used. The obtained data were compared with quantitative parameters of pulmonary MR perfusion in these patients.Statistics. Generalized additive model with beta distribution, Spearman correlation coefficient was used, Benjamini-Yekuteli correction was used to correct obtained p-values. Comparisons were determined as statistically significant when p<0.05. RESULTS: There was a correlation between quantitative CT data (fractions of pixels corresponding to non-ventilated and hypo-ventilated lung tissue) and the distribution of CT data into groups according to an empirical visual scale. We obtained a correlation between the functional perfusion parameters and the CT images: rMTT — 0.35 (p=0.001), rPBF — 0.23 (p=0.038) and rPBV — 0.35 (p=0.001).DISCUSSION: Using the algorithm of quantitative semi-automatic processing of CT-images suggested in this work allows to obtain numerical data, objectively reflecting percentage of affected lung tissue, that is especially relevant for diagnostics of COVID-19 pneumonia. The obtained correlation between functional perfusion parameters and CT picture can be potentially a marker of the lung pathological changes after COVID-19 pneumonia, that requires further investigations.CONCLUSION: Quantitative processing of CT-images allowed to correctly compare the CT scans of lung lesions in COVID-19 with MR lung perfusion data after COVID-19 infection which could potentially be of prognostic value.

An experimental setup and segmentation method for CFU counting on agar plate for the assessment of drinking water

Quantification of bacterial colonies on an agar plate is a daily routine for a microbiologist to determine the number of viable microorganisms in the sample. In general, microbiologists perform a visual assessment of bacterial colonies which is time-consuming (takes 2 min per plate), tedious, and subjective. Some automatic counting algorithms are developed that save labour and time, but their results are affected by the non-illumination on an agar plate. To improve this, the present manuscript aims to develop an inexpensive and efficient device to acquire S.aureus images via an automatic counting method using image processing techniques under real laboratory conditions. The proposed method (P_ColonyCount) includes the region of interest extraction and color space transformation followed by filtering, thresholding, morphological operation, distance transform, and watershed technique for the quantification of isolated and overlapping colonies. The present work also shows a comparative study on grayscale, K, and green channels by applying different filter and thresholding techniques on 42 images. The results of all channels were compared with the score provided by the expert (manual count). Out of all the proposed method (P_ColonyCount), the K channel gives the best outcome in comparison with the other two channels (grayscale and green) in terms of precision, recall, and F-measure which are 0.99, 0.99, and 0.99 (2 h), 0.98, 0.99, and 0.98 (4 h), and 0.98, 0.98, 0.98 (6 h) respectively. The execution time of the manual and the proposed method (P_ColonyCount) for 42 images ranges from 19 to 113 s and 15 to 31 s respectively. Apart from this, a user-friendly graphical user interface is also developed for the convenient enumeration of colonies without any expert knowledge/training. The developed imaging device will be useful for researchers and teaching lab settings.

Health System Considerations for Community-Based Implementation of Automated Respiratory Counters to Identify Childhood Pneumonia in 5 Regions of Ethiopia: A Qualitative Study.

In Ethiopia, childhood pneumonia is diagnosed in primary healthcare settings by measuring respiratory rate (RR) along with the presence of cough, chest indrawing, difficulty breathing, and fast breathing. Our aim was to identify health system-level lessons from implementing two automated RR counters, Children's Automated Respiration Monitor (ChARM) by Phillips® and Rad-G by Masimo®, to provide considerations for integrating such devices into child health programmes and health systems. This study was part of an initiative called the Acute Respiratory Infection Diagnostic Aids (ARIDA). Key informant interviews (KIIs) were conducted with 57 participants (health workers in communities and facilities, trainers of health workers, district management, and key decision-makers) in five regions of Ethiopia. Data were analyzed in ATLAS.ti using thematic content analysis and themes were categorized using the Tanahashi bottleneck analysis. All participants recommended scaling up the ARIDA initiative nationally as part of Integrated Management of Newborn and Childhood Illness (IMNCI) in primary healthcare. Health workers perceived the devices as: time saving, acceptable by parents and children, and facilitating diagnosis and referrals. Health workers perceived an increased demand for services and reduced numbers of sick children not seeking care. Participants recommended increasing the number of devices distributed and health workers trained. Strengthening drug supply chains, improving oxygen gas availability, and strengthening referral networks would maximize perceived benefits. While training improved knowledge, more supportive supervision, integration with current guidelines and more guidance related to community engagement was recommended. Automatic RR counters for the decentralized diagnosis of childhood pneumonia could have positive impact on improving the quality of diagnosis and management of pneumonia in children. However, the study has shown that a health system approach is required to ensure all steps along the pneumonia pathway are adequate, including drug and oxygen supply, community engagement, health worker training and support, and referral pathways.

Haematological Profiles of Plasmodium-Induced Changes in Serum Amyloid-A levels among the Internally Displaced Persons in Maiduguri

The menace of malaria has constituted a health challenge in Nigeria. Internally Displaced Persons’ Camps are available temporary accommodation provided for victims of war in war torn zones around the world. A study was undertaken to investigate relationship between Serum Amyloid A (SAA), haematological profiles and malaria infection among internally displaced person in three (3) different camps (GGC: Government Girls’ College Camp, GSS: Girls’ Secondary School Camp, CAN-: Christian Association of Nigeria) within Maiduguri, Borno state capital between March and September, 2017. A total of 400 sample subjects were recruited for the study, malaria parasite test was done using the Giemsa thick and thin peripheral blood film, SAA was analysed using the Enzyme linked immune-sobent assay (ELISA) kit while Haematological parameters were analyse using the automatic full blood count analyser. The result of the study shows a positive correlation between malaria infection and these parameters. A mean SAA of 32.76mg/l, PCV of 28.2%, haemoglobin of 8.88g/dl, platelets of 116.54×103 /µl, leucocyte of 6.02×103 /mm3 and neutrophils of 3.34×103 /mm3 was recorded in the positive subjects compared to the 14.11mg/l, 41%, 14.81g/dl, 261.46×103/µl, 8.23×103 /mm3 , 4.11×103 /mm3 reported in the negative patients group, this results shows that there are abnormal haematological alterations in malaria subjects compared to the negative control group, hence SAA and haematological parameters can be used as markers of plasmodium infection.

A multilevel decision-making approach for road infrastructure management

ABSTRACT The design of effective road safety countermeasures requires a network diagnosis supported by data. Moreover, infrastructures need to be ready for the introduction of vehicle-to-infrastructure communications to support technologies, as truck platooning. Ascendi, a motorway concessionaire, developed an action plan to decrease crash frequency and casualties that rests in data recorded by automatic vehicle counting devices. To have a good network representation, new equipment will provide more data, thus enhancing the selection of countermeasures. We developed a multilevel decision-support approach to define equipment location. The process stages correspond to three levels of analysis: (1) clustering for road segment classification (network level); (2) quantification of the devices to install ensuring similar coverage (concession level); (3) device allocation according to geographical and cost criteria (segment level). An iterative and participatory process involving Ascendi resulted in a proposal for adding 43 devices to the existing 72, increasing the network coverage to 39%.

Design of Automatic Truck Axle Counter Using Deep Learning on NVIDIA Jetson Nano

Design of Automatic Truck Axle Counter Using Deep Learning on NVIDIA Jetson Nano

Design Product Counting and Sorting Tools Using Esp8266-Based Volumenization

Indonesia is a large country, so it does not rule out the possibility of natural resources owned by Indonesia such as natural resources coal, petroleum, gas, nickel and many others When we enter the industrial world, especially placed in the production process area, more precisely the process of sorting goods, it is very necessary to optimize the performance and results of the effectiveness of these working hours so as to get high performance efficiency and will get maximum yield. In the shipping section there are several occurrences of problems which include three main activities of the process of receiving, storing and sending when receiving or sending. We recommend reducing errors at the time of delivery of goods. As for the problem of empty packaging and calculating the number of inappropriate products, by providing solutions using loadcell sensors for weight, proximity sensors for automatic counters and using google sheets as data storage in real time using esp8266 as a controller module.causing performance effectiveness that is expected to be better automatically and using the sophistication of IoT systems connected to the web system.and supported by several Components include relays, pneumatics, googlesheets in order to get accurate results, product sorting tools using this loadcell will be compared with loadcell weighing devices that have been on the market in order to find out how accurately it has obtained the appropriate results using a volume of 103gr. The results of the entire test tool get accurate data ranging from googlesheet to reading through Lcd 20x4 testing is carried out five times so that this tool can be applied to other supporting tools.that the entire tool is able to answer the problems of the developing industrial world.

Twice matched fruit counting system: An automatic fruit counting pipeline in modern apple orchard using mutual and secondary matches

Twice matched fruit counting system: An automatic fruit counting pipeline in modern apple orchard using mutual and secondary matches

P10.30.B COMBINATION TREATMENT WITH ERIBULIN AND TTFIELDS INDUCES APOPTOSIS AND G2/M CELL CYCLE ARREST IN HUMAN GLIOBLASTOMA CELLS

Abstract BACKGROUND TTFields has emerged to a first line treatment in newly diagnosed glioblastoma (GBM) patients. Several preclinical studies showed that the alternating electric fields inhibit proliferation and survival of human glioblastoma cells by disruption of the spindle apparatus and impairment of mitotic segregation. Eribulin is an antimitotic drug, that is currently used in the third line treatment of breast cancer. As it is also able to inhibit the pro-mitotic function of TERT, which is mutated in 80% of human glioblastoma, the drug was also shown to have a radiosensitizing and antiproliferative effect on human glioblastoma and ovarian cancer cell lines. This study aims to evaluate the effect of the combination of Eribulin and TTFields on the survival and cell cycle of glioblastoma cells. MATERIAL AND METHODS U87, A172 and four patient derived glioblastoma cell lines were cultured and plated to 80% confluence. Cells were then treated with TTFields at a frequency of 200kHz for 24 hours and/or received the ED50 and 0.5-fold ED50 of Eribulin. Controls remained untreated respectively. After 24 hours cells were harvested and counted by automatic cell count. Survival and cell cycle status were evaluated by FACS analysis. RESULTS The rate of early apoptotic cells was 82.28% in the group treated with TTFields and Eribulin. With TTFields treatment only 50.99% and with Eribulin alone 14.38% of cells were in an early apoptotic state. Untreated control cells had an apoptosis rate of 4.25%. The combination of TTFields and Eribulin was significantly more effective than the single treatment modalities (p>0.05). Treatment with TTFields increased the rate of cells an G2/M status. This effect was even stronger in the group with combination treatment. CONCLUSION Combination treatment of the antimitotic drug Eribulin with TTFields is able to improve the inhibitory effect of the drugs on cell survival. Furthermore the combination therapy effects the cell cycle of glioblastoma cells inducing G2/M arrest. Future preclinical studies should focus on the antimitotic effect and the influence of the treatment on genome integrity for a more detailed insight into the mechanisms of action.

Performance evaluation of the AiDx multi-diagnostic automated microscope for the detection of schistosomiasis in Abuja, Nigeria

In this research, we report on the performance of automated optical digital detection and quantification of Schistosoma haematobium provided by AiDx NTDx multi-diagnostic Assist microscope. Our study was community-based, and a convenient sampling method was used in 17 communities in Abuja Nigeria, based on the disease prevalence information extracted from the baseline database on schistosomiasis, NTD Division, of the Federal Ministry of Health. At baseline, samples from 869 participants were evaluated of which 358 (34.1%) tested S. haematobium positive by the reference diagnostic standard. Registered images from the fully automated (autofocusing, scanning, image registration and processing, AI image analysis and automatic parasite count) AiDx assist microscope were analyzed. The Semi automated (autofocusing, scanning, image registration & processing and manual parasite count) and the fully automated AiDx Assist showed comparable sensitivities and specificities of [90.3%, 98%] and [89%, 99%] respectively. Overall, estimated egg counts of the semi-automated & fully automated AiDx Assist correlated significantly with the egg counts of conventional microscopy (r = 0.93, p ≤ 0.001 and r = 0.89, p ≤ 0.001 respectively). The AiDx Assist device performance is consistent with requirement of the World Health Organization diagnostic target product profile for monitoring, evaluation, and surveillance of Schistosomiasis elimination Programs.

Evaluating the Performance of Video-Based Automated Passenger Counting Systems in Real-World Conditions: A Comparative Study.

Automatic passenger counting (APC) systems in public transport are useful in collecting information that can help improve the efficiency of transport networks. Focusing on video-based passenger counting, the aim of this study was to evaluate and compare an existing APC system, claimed by its manufacturer to be highly accurate (98%), with a newly developed low-cost APC system operating under the same real-world conditions. For this comparison, a low-cost APC system using a Raspberry Pi with a camera and a YOLOv5 object detection algorithm was developed, and an in-field experiment was performed in collaboration with the public transport companies operating in the cities of Turin and Asti in Italy. The experiment shows that the low-cost system was able to achieve an accuracy of 72.27% and 74.59%, respectively, for boarding and alighting, while the tested commercial APC system had an accuracy, respectively, of 53.11% and 55.29%. These findings suggest that current APC systems might not meet expectations under real-world conditions, while low-cost systems could potentially perform at the same level of accuracy or even better than very expensive commercial systems.

Image compression and reconstruction based on GUI and Huffman coding

Huffman coding is an important part of image compression technology, the image compression platform is based on GUI, and Huffman is also widely used. This paper introduces the basic principle of the Huffman algorithm, compares it with arithmetic coding and run length encoding, and expounds on the application of these three algorithms in JPEG compression. The AC algorithm combined block-based, fine texture models and adaptive arithmetic coding in the given an example. The RLE algorithm used automatic threshold, direction judgment, and selective value counts to improve its compression efficiency. JPEG algorithm adopted an adaptive quantization table to reduce the distortion rate of image compression. This paper proves the possibility of a better compression rate and distortion rate of the hybrid compression algorithm by demonstrating the improved example of the basic image compression algorithm. In the future, the improved basic algorithms can be combined based on the original JPEG algorithm, and different algorithms can be integrated on the GUI to face different use environments.

Impact of a lower demand during the COVID-19 pandemic on the frequency of dwell time delays

This study investigates the relationship between changes in the frequency of dwell time delays and passenger flow characteristics and the frequency of railway service during the COVID-19 pandemic. To do so we make use of automatic passenger count data from commuter trains in Southern Sweden. Our findings show that the frequency of dwell time delays decreased under COVID-19 conditions compared to the two years prior, but dwell time delays did not cease to exist altogether. The improvement in dwell time performance is found to occur for delays of 30 s and larger, whereas the occurrence of delays up to 15 s increased. No decline in the service frequency under COVID-19 conditions was found. Overall an improvement in dwell time punctuality can be observed under COVID-19 conditions. The analysis, somewhat, points towards a reduction in peak passenger loads to be one of the factors associated with this improvement. This is, however, not the sole reason as dwell time punctuality is found to improve for all volumes of boarding passengers. The lower passenger demand thus improved dwell time punctuality under COVID-19 conditions, but it does not tell the whole story. When trying to improve dwell time punctuality it is thus not sufficient to only reduce the number of passengers boarding, but other measures are necessary as well.

Automatic counting of lettuce using an improved YOLOv5s with multiple lightweight strategies

Automatic counting of lettuce using an improved YOLOv5s with multiple lightweight strategies

Face Recognition and Eye Blink Attendance Management System

This research explores the implementation and advantages of utilizing facial recognition technology for automated student attendance management in educational environments. The study outlines a comprehensive system that employs a high-resolution digital camera to detect and recognize students' faces, comparing them with stored images in a database. Successful matches result in real-time attendance logging, enhancing record-keeping and future analysis. The system also accommodates the inclusion of new images when no match is found. By embracing deep learning advancements, the technology offers an efficient alternative to conventional roll-call procedures, enabling automatic head counting in classrooms. The system integrates various face recognition algorithms, leveraging attributes such as shape, color, Local Binary Pattern (LBP), wavelet transforms, and auto-correlation. While challenges like varying lighting conditions, facial expressions, and face orientation are acknowledged, this research underscores the potential of technology to revolutionize attendance management and student engagement in education. Key Words: facial recognition technology, student attendance, automated system, deep learning, real-time logging, attendance management, classroom headcount, face recognition algorithms, record-keeping.

Deep Learning-Based Automatic Duckweed Counting Using StarDist and Its Application on Measuring Growth Inhibition Potential of Rare Earth Elements as Contaminants of Emerging Concerns.

In recent years, there have been efforts to utilize surface water as a power source, material, and food. However, these efforts are impeded due to the vast amounts of contaminants and emerging contaminants introduced by anthropogenic activities. Herbicides such as Glyphosate and Glufosinate are commonly known to contaminate surface water through agricultural industries. In contrast, some emerging contaminants, such as rare earth elements, have started to enter the surface water from the production and waste of electronic products. Duckweeds are angiosperms from the Lemnaceae family and have been used for toxicity tests in aquatic environments, mainly those from the genus Lemna, and have been approved by OECD. In this study, we used duckweed from the genus Wolffia, which is smaller and considered a good indicator of metal pollutants in the aquatic environment. The growth rate of duckweed is the most common endpoint in observing pollutant toxicity. In order to observe and mark the fronds automatically, we used StarDist, a machine learning-based tool. StarDist is available as a plugin in ImageJ, simplifying and assisting the counting process. Python also helps arrange, manage, and calculate the inhibition percentage after duckweeds are exposed to contaminants. The toxicity test results showed Dysprosium to be the most toxic, with an IC50 value of 14.6 ppm, and Samarium as the least toxic, with an IC50 value of 279.4 ppm. In summary, we can provide a workflow for automatic frond counting using StarDist integrated with ImageJ and Python to simplify the detection, counting, data management, and calculation process.